Implementation Details: Both segmentation models were trained and evaluated using a five-fold cross-validation on the training set. As in [1]}, the networks were trained for 1,000 epochs, where one epoch is defined as an iteration over 250 mini-batches. The batch sizes were 33 and 10 respectively for the CMR cine la4Ch and echocardiography apical 4Ch segmentation models. Stochastic gradient descent with Nesterov momentum (\(\mu \) =0.99) and an initial learning rate of 0.01 was used for learning network weights. The loss function used to train the `nnU-Net' model was the sum of cross-entropy and Dice loss. Data augmentation was performed on the fly and included techniques such as rotations, scaling, Gaussian noise, Gaussian blur, brightness, contrast, simulation of low resolution, gamma correction and mirroring. Please refer to [1]} for more details of the network training.
